Personalized Content Recommendations Based on Consumption Periodicity

ABSTRACT

Aspects described herein describe providing content recommendations such as, for example, recommendations for video content. A content recommendation may be based on when content was previously consumed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is a continuation of U.S. application Ser. No.16/750,998, filed Jan. 23, 2020, which is a continuation of U.S.application Ser. No. 15/905,366 entitled “Personalized ContentRecommendations Based on Consumption Periodicity” and filed on Feb. 26,2018, now U.S. Pat. No. 10,587,923, which is a continuation of U.S.application Ser. No. 14/540,663 entitled “Personalized ContentRecommendations Based on Consumption Periodicity” and filed on Nov. 13,2014, now U.S. Pat. No. 9,942,609 each of which is incorporated byreference herein in its entirety.

BACKGROUND

The selection of movies, television shows, games, music, and othercontent available to users continues to expand. To assist individuals inselecting content to consume, content providers may make recommendationsto the individuals. Techniques for improving those recommendations areneeded.

SUMMARY

Some of the various features described herein relate to systems andmethods for providing content recommendations. The content may be videocontent, audio content, text content, interactive content, and othertypes of content as well as combinations of such content.

Aspects described herein provide techniques for providing contentrecommendations such as, for example, recommendations for video contentprovided by a cable network. The content recommendation techniquesdescribed in further detail below determine the extent to whichpreviously consumed content should contribute to content considered forrecommendation to a user based on the consumption time of the previouslyconsumed content. The consumption habits of users have been observed tooccur at regular intervals. These intervals are employed to adjustcontribution factors for the previously consumed content when makingcontent recommendations.

As explained in further detail below, the value of a contribution factorfor previously consumed content is adjusted based on a time differencebetween the consumption time for that previously consumed item and areference time associated with the content items considered forrecommendation. In particular, the contribution factor is adjusted basedon the proximity of that time difference to a time period thatcorresponds to a content consumption interval. As one example, the timeperiod may be 24 hours, and the contribution factor for the previouslyconsumed content item may be adjusted based on the proximity of the timedifference to a multiple of 24 hours, which correspond to daily andweekly consumption interval. In some example implementations, thecontribution factor may be adjusted based on the proximity of the timedifference to a multiple of two time periods, for example, a multiple of24 hours as well as a multiple of 168 hours. Based, at least in part, onthe contribution factors of previously consumed content items,recommendation scores are determined for content items considered forrecommendation to a user. Content recommendations are then provided tothe user based on those recommendation scores.

This summary is not intended to identify critical or essential featuresof the disclosures herein, but instead merely summarizes certainfeatures and variations thereof. Other details and features will also bedescribed in the sections that follow.

BRIEF DESCRIPTION OF THE DRAWINGS

Some features herein are illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings and in whichlike reference numerals refer to similar elements.

FIG. 1 illustrates an example information access and distributionnetwork.

FIG. 2 illustrates an example computing platform on which variouselements described herein can be implemented.

FIG. 3 illustrates an example graph of viewing patterns generated fromviewing history data.

FIG. 4 illustrates another example graph of viewing patterns generatedfrom viewing history data.

FIG. 5 illustrates a flowchart of example method steps for providingcontent recommendations.

FIG. 6A illustrates an example of an implementation of a system thatprovides content recommendations.

FIG. 6B illustrates another example of an implementation of a systemthat provides content recommendations.

FIG. 7 illustrates an example workflow between components of an exampleof an implementation of a system that provides content recommendations.

FIG. 8 illustrates a flowchart of example method steps for providingcontent recommendations.

DETAILED DESCRIPTION

Features described herein relate to providing content recommendationsfor content-consuming users by adjusting a contribution factorassociated with previously consumed content for determining arecommendation score for candidate content recommended to an individual.As described in further detail below, the value of the contributionfactor is based, at least in part, on a time difference between theconsumption time of the previously consumed content and a reference timeassociated with the candidate content. In some example implementations,the value of the contribution factor may be relatively higher where thetime difference is relatively closer to a multiple of 24 hours andrelatively lower where the time difference is relatively further from amultiple of 24 hours. Stated differently, the contribution factor mayweight the contribution that the previously consumed content makes to arecommendation score for candidate content based on the proximity of thetime difference to a multiple of 24 hours. In other exampleimplementations, the contribution factor may be adjusted based onalternative periodicities or observed viewing patterns. Various ways ofusing this time difference are described further below, following thedescription of an example network and computing device on which featuresherein may be used.

FIG. 1 illustrates an example information access and distributionnetwork 100 on which many of the various features described herein maybe implemented. The network 100 may be any type of informationdistribution network, such as satellite, telephone, cellular, wireless,etc. One example may be an optical fiber network, a coaxial cablenetwork or a hybrid fiber/coax (HFC) distribution network. Such networks100 use a series of interconnected communication links 101 (e.g.,coaxial cables, optical fibers, wireless connections, etc.) to connectmultiple premises, such as homes 102, to a local office (e.g., a centraloffice or headend 103). The local office 103 may transmit downstreaminformation signals onto the links 101, and each home 102 may have areceiver used to receive and process those signals.

There may be one link 101 originating from the local office 103, and itmay be split a number of times to distribute the signal to various homes102 in the vicinity (which may be many miles) of the local office 103.These homes 102 may be in the same service area, such as a service areaof the local office 103. The homes 102 in the same service area may begeographically located within the same region(s). These homes 102 mayalso be physically and/or directly connected to the same networkutilized by the local office 103 to provide content (e.g., aninterconnection of the links 101). Although the term home is used by wayof example, the locations 102 may be any type of user premises, such asbusinesses, institutions, etc. The links 101 may include components notillustrated, such as splitters, filters, amplifiers, etc. to help conveythe signal clearly. Portions of the links 101 may also be implementedwith fiber-optic cable, while other portions may be implemented withcoaxial cable, other links, or wireless communication paths.

The local office 103 may include an interface 104, which may be atermination system (TS), such as a cable modem termination system(CMTS), which may be a computing device configured to managecommunications between devices on the network of the links 101 andbackend devices such as an edge device manager 105, an edge cache 106,and a content on-demand device 107 (to be discussed further below). Theinterface may be as specified in a standard, such as, in an example ofan HFC-type network, the Data Over Cable Service Interface Specification(DOCSIS) standard, published by Cable Television Laboratories, Inc.(a.k.a. CableLabs), or it may be a similar or modified device instead.The interface may be configured to place data on one or more downstreamchannels or frequencies to be received by devices, such as modems at thevarious homes 102, and to receive upstream communications from thosemodems on one or more upstream frequencies.

The local office 103 may also include one or more network interfaces108, which can permit the local office 103 to communicate with variousother local offices, devices, and/or network 109. The interface 108 mayinclude corresponding circuitry needed to communicate with other localoffices, devices, and/or networks. For example, the interface 108 mayfacilitate communications with other devices on networks such as acellular telephone networks and/or satellite networks.

The network 109 may include networks of Internet devices, telephonenetworks, cellular telephone networks, fiber optic networks, localwireless networks (e.g., WiMAX), satellite networks, and any otherdesired network. The network 109 may include and/or function as a cloudcomputing infrastructure comprising various processing and/or memorydevices (e.g., servers, databases, application providers, etc.). Thenetwork 109 may include one or more content delivery networks 130 thatmanage one or more content libraries that store content to be providedto users, such as at their homes 102. The content stored at the contentlibraries may be, for example, video-on-demand movies, streaming movies,television programs, songs, text listings, etc. A master content libraryof a content delivery network 130 may provide all the content availableto users at their homes 102. For example, if users have access to150,000 titles (e.g., on-demand movies and television shows), the150,000 titles may be stored at and/or provided by the master library.In some aspects, the master library might not store all of the contentavailable to users and/or subscribers. Instead, the master library maybe connected to one or more other content delivery networks of othercontent providers (not shown) in order to retrieve content requested byusers. The content providers may be managed, owned, and/or operated by aservice provider that manages, owns, and/or operates, for example, thecontent delivery network 130 or managed, owned, and/or operated byentities different from the service provider.

As noted above, the local office 103 may include a variety of devices,such as a content-on-demand device 105, an edge cache 106, and an edgedevice manager 107 that may be configured to perform various functions.For example, the content-on-demand device 105 may receive requests foron-demand content from users at the homes 102, through the interface104. As will be described in further detail in the examples below, thecontent-on-demand device 105 may process each received request andprovide content and/or a list of recommended content in response to therequests. The content-on-demand device 105 may include software tovalidate user identities and entitlements, locate and retrieve requesteddata, encrypt the data, and/or initiate delivery (e.g., streaming,downloading) of the content to the requesting user and/or device. Thecontent-on-demand device 105 may communicate with the edge cache 106and/or the edge device manager 107 to provide the content orrecommendation lists. For example, the content-on-demand device 105 maytransmit commands to the edge device manager 107 to either provide ornot to provide requested content and/or recommendation lists to theusers, as will be described in further detail in the examples below. Theedge cache 106 may be one or more computing devices that are configuredto store content to be provided to users in the homes. The contentstored at the edge cache 106 may be a subset of the content stored at acontent library of the content delivery network 130. For example, if acontent library of the content delivery network 130 stores and/orprovides the 20,000 titles mentioned above, the edge cache 106 may storeand/or provide 5,000 of those titles. Alternatively, some of the contentstored at the edge cache 106 might not overlap with the content storedat the content library of the content delivery network 130. For example,4,000 of the titles at the edge cache 106 may correspond to titlesstored at the content library of one content delivery network. Theremaining 1,000 might be stored at the content library of anothercontent delivery network. The edge device manager 107 may query one ormore databases, such as the edge cache 106 and/or content library of acontent delivery network 130 for content to be provided to users attheir homes 102.

The local office 103 may also include one or more additional computingdevices (e.g., servers). For example, the local office 103 may includean application server that may be a computing device configured to offerany desired service, and may run various languages and operating systems(e.g., servlets and JSP pages running on Tomcat/MySQL, OSX, BSD, Ubuntu,Redhat, HTML5, JavaScript, AJAX and COMET). For example, an applicationserver may be responsible for collecting data such as television programlistings information and generating a data download for electronicprogram guide listings. Another application server may be responsiblefor monitoring user viewing habits and collecting that information foruse in selecting advertisements. Another application server may beresponsible for formatting and inserting advertisements in a videostream being transmitted to the homes 102. These application servers mayprovide services in conjunction with content and/or recommendation listsprovided to users.

The content stored at each local office 103 (e.g., within an edge cache)may differ from the content stored at other local offices. In someaspects, the content stored (e.g., cached or otherwise temporarilystored) at each local office may dynamically vary based on the servicearea of the respective local office. The service area of the localoffice 103 may include the homes 102 illustrated in FIG. 1. In someaspects, the service area may be defined by a physical content deliverynetwork. For example, the homes 102 may be part of the service area ofthe local office 103 because they are physically and/or directlyconnected to the local office by one or more communication lines 101(coaxial cables, optical fibers, wireless connections, etc.). A home 102may be part of more than one service area. For example, if a home 102 ais also physically and/or directly connected to the local office 103,the home 102 a may also be part of the service area of local office 103.In some aspects, the demographics and/or interests in content within aservice area may be similar. For example, a local office that serves asuburban area may serve primarily single-family homes and/or dwellingshaving families. A local office in an urban area, such as a large city,may serve primarily young adults that live in those areas. Because thedemographics of each service area may be unique to that service area,content requested by users within each service area may be similar.Content requests by users in different service areas, on the other hand,may differ significantly. For example, suburban users may includechildren and/or parents interested in children's programs. Urban users,on the other hand, may be more interested in new and/or populartelevision shows than children's programs. As will be described infurther detail in the examples below, content stored at a particularedge cache, such as the edge cache 106, and/or content recommendationsprovided to users may be based on the service area and/or the localoffice 103 that the user is part of and/or connected to.

An example home 102 a may include an interface 120. The interface maycomprise a device 110, such as a modem, which may include transmittersand receivers used to communicate on the links 101 and with the localoffice 103. The device 110 may be, for example, a coaxial cable modem(for coaxial cable links 101), a fiber interface node (for fiber opticlinks 101), or any other desired modem device. The device 110 may beconnected to, or be a part of, a gateway device 111. The gateway device111 may be a computing device that communicates with the device 110 toallow one or more other devices in the home to communicate with thelocal office 103 and other devices beyond the local office. The gatewaydevice 111 may be a wireless or wired router, set-top box (STB), digitalvideo recorder (DVR), computer server, or any other desired computingdevice. The gateway device 111 may also include (not shown) localnetwork interfaces to provide communication signals to the devices 112a-n in the home, such as televisions, additional STBs, personalcomputers, laptop computers, wireless devices (wireless laptops andnetbooks, mobile phones, mobile televisions, personal digital assistants(PDA), etc.), and any other desired devices. These devices (along withthe gateway device 111 and/or the modem 110) may be used to requestcontent. Examples of the local network interfaces include MultimediaOver Coax Alliance (MoCA) interfaces, Ethernet interfaces, universalserial bus (USB) interfaces, wireless interfaces (e.g., IEEE 802.11),Bluetooth interfaces, and others.

The various devices described herein may be computing devices, and FIG.2 illustrates general hardware elements that can be used to implementany of the various computing devices discussed herein. The computingdevice 200 may include one or more processors 201, which may executeinstructions of a computer program to perform any of the featuresdescribed herein. The instructions may be stored in any type ofcomputer-readable medium or memory, to configure the operation of theprocessor 201. For example, instructions may be stored in a read-onlymemory (ROM) 202, a random access memory (RAM) 203, a hard drive,removable media 204, such as a Universal Serial Bus (USB) drive, compactdisk (CD) or digital versatile disk (DVD), floppy disk drive, or anyother desired electronic storage medium. Instructions may also be storedin an attached (or internal) hard drive 205. The computing device 200may include one or more output devices, such as a display 206 (or anexternal television), and may include one or more output devicecontrollers 207, such as a video processor. There may also be one ormore user input devices 208, such as a remote control, keyboard, mouse,touch screen, microphone, etc. The computing device 200 may also includeone or more network interfaces, such as input/output circuits 209 (suchas a network card) to communicate with an external network 210. Thenetwork interface may be a wired interface, wireless interface, or acombination of the two. In some embodiments, the interface 209 mayinclude a modem (e.g., a cable modem), and network 210 may include thecommunication links 101 discussed above, the external network 109, anin-home network, a provider's wireless, coaxial, fiber, or hybridfiber/coaxial distribution system (e.g., a DOCSIS network), or any otherdesired network.

Content, as used in this description, refers to any type of media thatmay be consumed by an individual. Examples of content that may beconsumed include video content, audio-only content, image content, textcontent, interactive content, and the like. Video content may alsoinclude audio and thus additionally be referred to as audiovisualcontent. In addition content includes, for example, broadcasted content,transmitted content, and recorded content. Furthermore content includescontent that is provided, for example, at a scheduled broadcast ortransmission time, on demand in response to receipt of a request for thecontent, and as playback from content recorded and stored on a recordingdevice or other type of data storage device. Some non-limiting examplesof content include television broadcasts, radio broadcasts, webbroadcasts, video-on-demand, and digitally recorded content. Asdescribed in further detail below, the techniques for providing contentrecommendations are based on content previously consumed by anindividual. As used in this description, consuming content refers to,for example, listening to audio content, viewing video content and imagecontent, reading text content, and interacting with interactive content.

The proposed approach for making content recommendations is described infurther detail below with respect to video content including videocontent scheduled for broadcast at a particular broadcast time,on-demand video content, and digitally recorded video content. With thebenefit of this disclosure, it will be appreciated that the approach toproviding video content recommendations described below may be adaptedfor providing recommendations of other types of content. Varioustechniques may be selectively employed for making contentrecommendations such as, for example, video content recommendations. Insome example implementations, content recommendations may be based on adetermined similarity between candidate content and previously consumedcontent. In these example implementations, a similarity score for apairing of the candidate content and previously viewed content isobtained, and the recommendation score for the candidate content isbased, at least in part, on that similarity score. In other exampleimplementations, the recommendation score may be obtained for a contentmenu rather than specific content. Content menus may correspond tocontent format (e.g., audio, video, text, etc.), content type (e.g.,movie, television series, sports, etc.), content genre (e.g., drama,comedy, horror, etc.), and so forth. In these other exampleimplementations, the recommendation score may be obtained for a contentmenu. In each example, the recommendation scores are further based onone or more contribution factors obtained for previously viewed content.The contribution factor is adjusted based on a time difference between aconsumption time of the previously viewed content and a reference time.Generally stated, the techniques for adjusting the contribution factorof previously consumed content described herein may be employed invarious techniques to making recommendations based on previouslyconsumed content.

As noted above and described in further detail below the contentrecommendations are based on content previously consumed by anindividual. The previously consumed content may be stored in aconsumption history that identifies a consumption time of the consumedcontent. The consumption time may be the time the individual startedconsuming the content or finished consuming the content. In some exampleimplementations, the consumption history may include both theconsumption start time and the consumption finish time. With respect tovideo content, for example, the consumption history may be a viewinghistory of video content previously viewed by the individual. Thepreviously viewed video content may include video programs (e.g.,movies, television shows, etc.) selected by the viewer for viewing ondemand, video programs provided at a prescheduled broadcast time, andvideo programs recorded to a recording device such as, e.g., a digitalvideo recorder (DVR) for subsequent playback. The content provider maymaintain the viewing history for a viewer that includes respectiveentries that each identify a video program viewed by the viewer and atimestamp indicating the date and time the video program was viewed bythe viewer. For content scheduled for broadcast, this time may be thebroadcast time. For on-demand content, this time may be the time theuser requested the on-demand content or completed a viewing of theon-demand content. For recorded content, this time may be the time anindividual initiated playback of the recorded content or completed aviewing of the recorded content. A gateway device (such as gatewaydevice 111) may submit an update to the viewing history when anindividual selects or tunes to broadcasted content. An on-demand device(such as on-demand device 105) may submit an update the viewing historyfor a viewer whenever a viewer requests to view a video program ondemand or completes a viewing of the on-demand video program. Arecording device (such as a DVR) may also submit an update to theviewing history when playback of recorded content is initiated or whenplayback of the recorded content is complete. The application server ofa local office (such as local office 103) that monitors the viewinghabits of a viewer may manage the updates to the viewing history for aviewer when the viewer views a video program. Content recommendationsfor other types of content may be provided in a similar fashion usingconsumption histories configured for those types of content, e.g., alistening history for audio content, a reading history for text content,and an interaction history for interactive content.

A content provider may compile large volumes of data related to theconsumption habits of respective individuals. The content provider maythus leverage this consumption history data when generating contentrecommendations. In particular, the consumption histories of individualshave been analyzed, and the insights acquired through these analyseshave been utilized to implement an approach to providing contentrecommendations that advantageously improves the recommendationspresented to individuals including recommendation techniques that arebased on the similarity between candidate content and previouslyconsumed content. Similar techniques may be employed to improve othertechniques for making content recommendations such as, for example,content menu recommendations as noted above. FIG. 3 and FIG. 4 belowillustrate the relationships that have been discovered between viewingpatterns and content similarity.

Referring now to FIG. 3, a graph 300 of viewing patterns generated fromviewing history data is shown. To generate the graph 300, multipleviewing histories were compiled based on data collected from multiplegateways respectively located at multiple households (e.g., gateways 111located at homes 102). As noted above, the viewing history may includean entry for each viewing, V, of video content, C, that was viewed at aparticular date and time, T. Accordingly each entry in the viewinghistory may be designated using the 2-tuple, V {C, T}.

To generate the data points for the graph 300, each viewing, V, of aviewing history was paired with each other viewing in the viewinghistory. As an example, consider a first viewing history havingviewings, V₁, V₂, V₃, and V₄. The viewing pairings, in this example,would thus include: {V₁, V₂}, {V₁, V₃}, {V₁, V₄}, {V₂, V₃}, {V₂, V₄},and {V₃, V₄}. For each pairing, the time difference, ΔT, between viewingthe video content, C_(x) and C_(y), was calculated, e.g., the absolutevalue of the difference in hours between the viewing times, ΔT=|T₁−T₂|.Accordingly, each pairing, P, of viewings, V, may be designated usingthe 3-tuple, P={C_(x), C_(y), ΔT}.

Each data point in the graph 300 thus corresponds to a pairing, P. Thex-axis 302 plots ΔT of each pairing, P, as the number of hours thatelapsed between viewings of the paired video content, C_(x), and C_(y).The y-axis 304 plots the frequency of the time difference, ΔT, in thepairings generated. The graph 300 thus illustrates the total number ofpairings, P, where the viewings of the content, C_(x) and C_(y), were ΔThours apart.

Based on the graph 300, several methods for recommending content areproposed. As seen in the graph 300, weekly viewing habits tend to repeatwith content viewings occurring at around the same time each week. Thegraph 300 includes respective peaks 306 at about 168 hours, 336 hours,504 hours, 672 hours, and 840 hours. It will be appreciated that a weekis 168 hours long (24 hours/day×7 days/week=168 hours/week). Thereforethe weekly peaks 306 correspond to viewings that are around the sametime and that are about one week to about five weeks apart. As also seenin the graph 300, daily viewing habits tend to repeat with contentviewings occurring at around the same time each day. The graph 300includes six respective peaks 308 between each of the weekly peaks 306.It will thus be appreciated that each peak 308 corresponds to viewings24 hours apart. Therefore the daily peaks 308 correspond to viewingsthat are around the same time each day. Improved methods of makingcontent recommendations are thus proposed that leverage theseobservations. For the sake of clarity not all of the daily peaks 308have been labeled in FIG. 3.

The pairings, P, used to generate the graph 300 of FIG. 3 plotted thenumber of viewed program pairs against the time differences between theprograms in each pair. The pairings do not consider the similaritybetween the video content, C_(x), and C_(y), of the paired viewings.Taking into consideration the similarity of the video content of thepairing has provided additional observations regarding the viewinghabits of viewers.

Referring now to FIG. 4, an example graph 400 of viewing patternsgenerated from viewing history data as well as video content similarityis shown. To generate the graph 400, multiple viewing histories werecompiled and viewings, V, were paired together as described above withreference to FIG. 3. To generate the data points for graph 400, however,a similarity score, S, was calculated for the video content, C_(x), andC_(y), of each pairing. Determining similarity scores is discussed infurther detail below. The similarity score, S, was thus included withthe video content, C_(x), and C_(y), and the time difference, ΔT,between viewings to obtain an extended pairing, P′. Accordingly theextended pairing, P′, may be designated using the 4-tuple, P′={C_(x),C_(y), ΔT, S}.

Each data point in the graph 400 thus corresponds to an extendedpairing, P′. The x-axis 402 plots each extended pairing, P′, as thenumber of hours elapsed, ΔT, between viewings of the paired videocontent, C_(x), and C_(y). The y-axis 404 plots the frequency of thesimilarity score, S, in the extended pairings generated. The graph 400thus illustrates the total number of extended pairings, P′, where thevideo content, C_(x), and C_(y), have a similarity score, S, and wereviewed ΔT hours apart.

Based on the graph 400, the inventors arrived at additional conclusionswith respect to viewing habits of viewers and the relationship betweenthose viewing habits and content similarity. Like the graph 300 in FIG.3, the graph 400 includes respective weekly peaks 406 (collectively) andrespective daily peaks 408 (collectively) for the similarity scores, S.Again for the sake of clarity, not all daily peaks 408 have been labeledin FIG. 4. The daily peaks 408 on the graph 400 indicate that videocontent viewed around the same time each day (e.g., some multiple of 24hours apart) is relatively more similar than content viewed for othertime differences (e.g., 10, 12, 18, 20, etc., hours apart). The weeklypeaks 406 similarly indicate that video content viewed around the sametime each week (e.g., some multiple of 168 hours apart) is alsorelatively more similar than content viewed for other time differences(e.g., 158, 178, 326, 346 494, 514, etc., hours apart). The graph 400further indicates that video content viewed around the same time eachweek is relatively more similar than video content viewed around thesame time each day. As also seen in the graph 400 of FIG. 4, thesimilarity scores associated with the weekly peaks 406 are relativelyhigher than the similarity scores associated with the daily peaks 408,and the similarity scores for the weekly/daily programs were higher thanother program pairings that were not weekly or daily.

The similarity scores for the extend pairings also exhibit an overalldecay as the time difference between viewings increases. Stateddifferently, the graph 400 indicates that the similarity score isinversely proportional to the number of hours that elapse betweenrespective viewings of video content. In the graph 400 of FIG. 4,similarity scores for six weeks 410 a-f of extending pairings of videocontent are shown. The time difference, ΔT, between 0 and 168corresponds to the first week 410 a; between 168 and 336 hourscorresponds to the second week 410 b; between 336 hours and 504 hoursfor the third week 410 c; and so forth respectively for the fourth week410 d, the fifth week 410 e, and the sixth week 410 f. As seen in FIG.4, the average similarity score gradually decreases between the firstweek 410 a and the sixth week 410 f. In particular, the graph 400illustrates that the similarity score for the weekly peak 406 a at theend of the first week 410 a is higher than the similarity score for theweekly peak 406 b at the end of the second week 410 b which is, in turn,higher than the similarity score for the weekly peak 406 c at the end ofthe third week 410 c, and so forth through the sixth week 410 f. Inaddition, the graph 400 illustrates that the average similarity scorefor the daily peaks 408 a during the first week 410 a is higher than theaverage similarity score for the daily peaks 408 c during the third week410 c which is, in turn, higher than the average similarity score forthe daily peaks 408 f during the sixth week 410 f. Accordingly the graph400 indicates that the similarity between video content decreases as thetime that elapses between viewing the video content increases.

With respect to the similarity between the video content paired, variousapproaches may be selectively employed to determine the similarityscore. In some example implementations, the similarity between pairedvideo content may be based on metadata for the content, e.g., contenttype (movie, television series, news program, etc.), genre (drama,action, horror, romance, etc.), release date, actors, and other types ofcontent metadata that will be appreciated by those skilled in the art ofcontent distribution. In these examples, video content may be determinedto be relatively more or less similar depending on which and/or how manyitems of metadata they have in common.

In other example implementations, the similarity score between pairedvideo content may be based on the number of viewers that have previouslywatched both programs. For convenience, the function for obtaining asimilarity score, S, for a pairing, P, of content, C_(x) and C_(y), maybe expressed as S(C_(x), C_(y)), For example, consider video content C₁,C₂, and C₃. The similarity score, S₁, for the pairing, P₁, of the videocontent, C₁ and C₂, may be based on the total number of individuals thatpreviously watched both video content C₁ and video content C₂. Likewisethe similarity score, S₂, for the pairing, P₂, of the video content, C₁and C₃, may be based on the total number of people that previouslywatched both video content C₁ and video content C₃. If more peoplewatched both video content C and C₂ as compared to video content C₁ andC₃, then the similarity score, S₁(C₁, C₂), for the pairing, P₁, would behigher than the similarity score, S₂(C₁, C₃), for the pairing, P₂.Determining a similarity scores is described in further detail incommonly-owned U.S. patent application Ser. No. 13/277,482 entitled“Recommendation System” and filed on Oct. 20, 2011 which published asU.S. Patent Application Publication No. 2013/0103634 on Apr. 25, 2013and which is incorporated by reference herein in its entirety.

The insights acquired from the graph 300 of FIG. 3 and the graph 400 ofFIG. 4 have been applied to a recommendation process that utilizescontent similarity when generating a list of recommended video contentfor a viewer. As described in further detail below, the recommendationprocess may track the content that a user consumes. For each piece ofconsumed content, the process may entail storing information identifyingthe content and the time the user consumed the content. The process mayfurther select candidate content for recommendation to a viewer. Thecandidate content selected may be based on the type of content, e.g.,on-demand content, broadcast content, recorded content, etc. In someimplementations all candidate content may be selected forrecommendation. In other example implementations, the candidate contentselected may first be filtered from a pool of candidate content, e.g.,filtered by content format, content type, content cost, content genre,content release date, performer, and other types of criteria suitable tofilter the pool of candidate content. The pool of candidate content maybe filtered, for example, in response to a content search performed byan individual. The candidate content can also be selected by identifyingcontent the user has not yet consumed, i.e., unconsumed content.

As noted above one type of recommendation process may utilize contentsimilarity to make content recommendations. This example recommendationprocess may entail generating content pairings by pairing candidatecontent with previously consumed content in a consumption history. Foreach pairing, the recommendation process may generate a similarity scoreand determine a time difference for the pairing. The time difference maybe the total number of hours that have elapsed between the time the userconsumed the previous content—i.e., the consumption time—and a referencetime associated with the candidate content. As described in furtherdetail below, the reference time for the candidate content may depend onthe content type of the candidate content. The recommendation processmay then adjust a contribution factor for the previously consumedcontent in order to change the contribution the previously consumedcontent makes to the recommendation score for the candidate content. Inthis way the contribution factor functions as a weight for thepreviously consumed content. The value of the contribution factor, inthis example, is based, at least in part, on the proximity of the timedifference to an integer multiple of 24 hours or 168 hours. In addition,the contribution factor may be adjusted based on the decay of thesimilarity score as the time difference increases as observed andillustrated in the graph 400 of FIG. 4. In other words the decay of thesimilarity score may be inversely proportional to the time difference.The recommendation score for the candidate content may then be obtainedby aggregating the respective contribution factors and similarity scoresfor each pairing. Aggregation may include summing the respectiveproducts of the contribution factors and similarity scores as explainedin further detail below.

The graph 400 shown in FIG. 4 is just one example of the results thatmay be obtained through an analysis of the viewing histories compiled bya content provider. As illustrated in the graph 400 and described above,the inventors have discovered a relationship between video contentsimilarity and a difference between consumption times. Other analyses ofconsumption histories may reveal relationships between othercharacteristics associated with the content and the difference betweenconsumption times of that content. For example, an analysis ofconsumption histories with respect to content format (e.g., audio,video, text, interactive, etc.), content type (e.g., on-demand,broadcast, recorded, etc.), content cost (e.g., paid, free), and soforth may reveal alternative viewing patterns and alternativeperiodicities (i.e., viewing patterns that do not conform to multiplesof 24 hours). The relationship graphs of these alternative viewingpatterns and alternative periodicities may thus exhibit different curvesrelative to the curve seen in the graph 400 of FIG. 4. Nevertheless thecontributions of previously consumed content may be adjusted based onthese alternative viewing patterns and alternative periodicities toimprove other content recommendation techniques that are based onalternative characteristics.

For recommendation techniques that are based on content similarity, thesimilarity may be a value quantifying the similarity between candidatecontent and previously consumed content and expressed by the variable,S. The contribution factor may function to weight the contributionpreviously consumed content makes to a recommendation score and may beexpressed by the variable, W. As described above, the contributionfactor, W, in this example, is based on the proximity of the timedifference to an integer multiple of 24 hours. The value of thecontribution factor may be highest for programs where the timedifference is an integer multiple of 168 hours (a week) and 24 hours (aday), and may be progressively lower as the time difference departs fromthe 24 hour and 168 hour multiples.

The recommendation score, R, for candidate content, C, may be expressedas a sum of the respective contribution factors, W, and the similarityscores, S, for pairings of the candidate content, C, and eachpreviously-consumed content, V₁ . . . V_(n), in the consumption history.The recommendation score, R, may be expressed as the following:

R=1+W ₁ S ₁ +W ₂ S ₂ + . . . +W _(n) S _(n)

In the example formula above, S₁ represents the similarity score for thepairing of content, C, and previously viewed content, V₁−S(C, V₁); S₂represents the similarity score for the pairing of content, C, andpreviously viewed content, V₂−S(C, V₂); and S_(n) represents thesimilarity score for the pairing of content, C, and previously viewedcontent, V_(n)−S(C, V_(n)). Other formulas for obtaining therecommendation score, R, may be selectively employed. In some exampleimplementations, obtaining the recommendation score may includedetermining a popularity score for the candidate content andincorporating the popularity score in the formula for the recommendationscore. For convenience the function for obtaining a popularity score, O,for candidate content, C, may be expressed as O(C). The popularityscore, O, may be, for example, the percentage of all users that consumedthe content, C. In these other implementations, a recommendations score,R′, may be expressed as:

R′=O×(1+W ₁ S ₁ +W ₂ S ₂ + . . . +W _(n) S _(n))

The pseudocode below may be employed to obtain a contribution factor forpreviously consumed video content based on the relationship betweencontent similarity and consumption time observed from the graph 400 inFIG. 4. The values in the pseudocode below were obtained by performingregression and curve fitting techniques to the curve illustrated in thegraph 400.

TABLE 1 EXAMPLE PSEUDOCODE FOR OBTAINING CONTRIBUTION FACTOR timeDiff =time₂ − time₁; dayHourDiff = | (timeDiff % 24) − 12) |; weekDayDiff = |[(timeDiff ÷ 24) % 7] − 3.5 |; feature[1] = timeDiff {circumflex over( )} −0.399 // overall decay feature[2] = (dayHourDiff ≥ 11) ? timeDiff{circumflex over ( )} −0.448 : 0; // peak decay feature[3] = dayHourDiff{circumflex over ( )} 2 // day booster feature[4] = weekDayDiff{circumflex over ( )} 2 // week booster feature[5] = 1 // constantcoefficients = { 10.705, 5.6732, 0.0009, 0.0067, 0.0472 }; contribution= 0; for each i = 1 ... 5 contribution = contribution + (features[i] ×coefficients[i] );

In the pseudocode above, the timeDiff value is the difference betweenthe consumption time of previously consumed content and a reference timeassociated with candidate content as described above. The dayHourDiff isconfigured such that its value is higher where the timeDiff value iscloser to a multiple of 24 hours and lower where the timeDiff value isfurther from a multiple of 24 hours. For example, the dayHourDiff valuewill be around 12 when then timeDiff value is around a multiple of 24hours and will be around 0 when the timeDiff value is around a multipleof 24 hours plus or minus 12 hours (i.e., the furthest the timeDiff canbe from a multiple of 24 hours). Similarly the weekDayDiff is configuredsuch that its value is higher where the timeDiff value is closer to amultiple of 168 hours (i.e., 7 days) and lower where the timeDiff valueis further from a multiple of 168 hours. For example, the weekDayDiffvalue will be around 3.5 when the timeDiff value is around a multiple of168 hours and will be around 0 when the timeDiff value is around amultiple of 168 hours plus or minus 84 hours (i.e., 3.5 days which isthe furthest the timeDiff value can be from a multiple of 168 hours). Inthis way, the pseudocode shown by way of example achieves generation ofa contribution factor that conforms to the daily and weekly viewingperiodicities observed and illustrated in the graph 400 of FIG. 4. Thepseudocode, in this example, also employs various features to accountfor the additional observations in the viewing patterns observed fromthe graph 400 of FIG. 4. The first feature, feature[1], is configuredand employed to account for the overall decay in content similarity asthe time difference increases observed in the graph 400 of FIG. 4. Thesecond feature, feature[2], is configured and employed to account forthe sharp decline in similarity from the daily peaks observed in thegraph 400 of FIG. 4 (e.g., peaks 408). The third and fourth features,feature[3] and feature[4], are employed to boost the contribution factorwhere the timeDiff is close to a multiple of 24 hours or 168 hoursrespectively. By squaring the dayHourDiff and the weekDayDiff thecontribution factor for previously consumed content will be boosted whenthat content was consumed some multiple of 24 hours or 168 hourspreviously thereby accounting for the daily and weekly peaks observed inthe graph 400 of FIG. 4. The fifth feature, feature[5], represents aconstant that may be selectively employed. The pseudocode, in thisexample, also employs a set of coefficients in order to weight thevarious features in the calculation of the contribution score. As seenin the table above, the contribution is the sum total of each featuremultiplied by its corresponding coefficient.

The pseudocode above is provide by way of example as one way ofobtaining a contribution factor for a recommendation technique thatutilizes content similarity when making content recommendations. Otherapproaches for obtaining a contribution factor may be selectivelyemployed for other types of recommendation techniques that rely onadditional or alternative content characteristics when making contentrecommendations. As noted above, the values employed for the featuresand coefficients, in this example, were obtained through regression andcurve fitting techniques applied to the graph 400 of FIG. 4. Otherimplementations may employ regression and curve fitting techniques toobtain suitable values and coefficients for other relationship curvesthat are observed.

The content being considered for recommendation, C, may be paired withone, some, or all previously consumed content appearing in theconsumption history. For example, in some implementations, therecommendation process may limit the content pairings to contentpreviously consumed within the past x weeks (e.g., 6 weeks). The timedifference threshold may depend on the processing power available at thedevice generating the recommendation scores and a desired response time.For example, the time difference threshold may be set relatively lower(e.g., 2-3 weeks) when a relatively faster response time is desired inorder to reduce the number of pairings when determining therecommendation score.

Referring now to FIG. 5, a flowchart of an example method 500 forproviding content recommendations based on content similarity is shown.A recommendation system (FIG. 6A-B) may be configured and arecommendation processor may initiate the recommendation process (block502). Configuring the recommendation system may include, for example,compiling and storing the consumption history for the individual. Duringinitiation, the recommendation processor may locate the consumptionhistory for a user and may locate content to consider forrecommendation, e.g., one or more video programs. The recommendationprocess may be initiated automatically on a periodic basis (e.g., as anautomatic recommendation task) or in response to various triggers. Insome example implementations, the recommendation processor may beconfigured to initiate the recommendation process on a daily, weekly, ormonthly basis. Other periods may be selectively employed. Therecommendation processor may, additionally or alternatively, beconfigured to initiate the recommendation process in response to receiptof an explicit recommendation request received at a gateway device(e.g., the gateway device 111) from an individual. The recommendationprocessor may, additionally or alternatively, be configured to initiatethe recommendation process in response to receipt of an on-demandrequest received at a gateway device from an individual. Therecommendation processor may, additionally or alternatively, beconfigured to initiate the recommendation process in response todetecting the individual has accessed a programming guide at the gatewaydevice, e.g., an electronic programming guide (EPG) or an interactiveprogramming guide (IPG). Additional and alternative types of events maytrigger the recommendation process.

Having initiated the recommendation process, the recommendationprocessor may select candidate content to consider for recommendation tothe individual (block 504). As described above, the content selected forrecommendation may be on-demand content, content scheduled forbroadcast, or digitally recorded content. As also mentioned above, thecontent selected for recommendation may be video content, audio content,text content, interactive content, and so forth. The video content maybe a movie, a movie trailer, an episode of a television series, a newsprogram, a video clip, and other types of video content. Therecommendation processor may then retrieve the consumption history forthe individual (block 506) and select one item of previously consumedcontent from the consumption history (block 508). The recommendationprocessor may then pair the content selected for recommendation with thepreviously consumed content (block 510) as described above. Therecommendation processor may then obtain a similarity score for thepairing of the selected content (block 512) as also described above. Therecommendation processor itself may implement the functionality todetermine the similarity score. Alternatively the recommendationprocessor may request and receive the similarity score from a similarityprocessor that determines the similarity score.

The recommendation processor may then calculate the time difference forthe paired content (block 514). As described above, the time differencemay be provided as the number of hours that have elapsed between theconsumption time of the previously consumed content and a reference timeassociated with the candidate content. As also noted above, thereference time for the candidate content may depend on the content typeof the candidate content. Where the candidate content is prescheduledcontent, the reference time may be a time at which the prescheduledcontent is scheduled for broadcast. Where the candidate content isdigitally recorded content, the reference time may be a time at whichthe individual accesses a playback feature of the recording device andviews a set of digitally recorded content available for playback. Wherethe candidate content is on-demand content, the reference time may be atime at which the individual accesses an on-demand feature of a gatewaydevice and views a set of on-demand content available for selection. Insome example implementations, the reference time may be the time atwhich the recommendation processor selects the candidate content, i.e.,the time at which the recommendation process occurs.

The recommendation processor may then select a weight for the pairing ofthe selected content and bias that weighting based on the calculatedtime difference (block 516). As described above, biasing the weight mayinclude increasing the weight as the time difference approaches amultiple of a first predetermined time period (e.g. 24 hours) anddecreasing the weight as the time difference departs from a multiple ofthe first predetermined time period (e.g., 24 hours). Biasing the weightmay also include increasing the weight further, i.e., to a greaterextent when the time difference approaches a multiple of a secondpredetermined time period that is also a multiple of the firstpredetermined time period. As noted above, the first predetermined timeperiod may be 24 hours, and the second predetermined time period may be168 hours (e.g., a week long time difference). Stated differently, atime-series of weight values plotted with respect to time difference mayexhibit increasing weight values as the time difference approaches amultiple of the first predetermined time period (e.g., approaches 24hours) and exhibit a peak weight value where the time difference isexactly a multiple of the first predetermined time period (e.g., isexactly a multiple 24 hours). The time-series of weight values may alsoexhibit decreasing weight values as the time difference departs from amultiple of the first predetermined time period (e.g., departs from amultiple of 24 hours) and exhibit a valley weight value where the timedifference is exactly between two multiples of the first predeterminedtime period (e.g., exactly between two multiples of 24 hours, i.e., amultiple of 24 hours plus 12 hours). The weight values may then begin toincrease again as the time difference departs from this middle point andagain approaches the next multiple of the first predetermined timeperiod (e.g., 24 hours). Since the weight value correlates with the timedifference, in this example, the proposed approach provides improvedrecommendations even when the time difference isn't exactly a multipleof the first predetermined time period (e.g., 24 hours), for example,where the time difference is a multiple of the first predetermined timeperiod plus or minus x number of minutes (e.g., −0.5 hours, +0.5 hours,etc.). Biasing the weight may further include applying a decay factor tothe weight. As described above, the decay factor, in some exampleimplementations, may be inversely proportional to the time differencesuch that the decay factor decreases as the time difference increases.

Having obtained the initial similarity score and the weight, therecommendation processor may then obtain a weighted similarity score forthe pairing of the previously-consumed content with the selectedcandidate content (block 518) as also described above. If the viewinghistory includes more previously-consumed content to be paired with thecandidate content (block 520:Y), then the recommendation processor mayselect the next previously-consumed content (block 522) and repeat thesteps described above to obtain another weighted similarity score forthe new pairing of content. Once no more previously-consumed contentremains to be paired with the candidate content selected (block 520:N),the recommendation processor may aggregate the weighted similarityscores (block 524) and generate a recommendation score for the candidatecontent based on the aggregated weighted similarity scores (block 526).

The recommendation processor may make more than one recommendation tothe viewer. Accordingly if additional candidate content is available forrecommendation (block 528:Y), the recommendation processor may selectnew candidate content to recommend to the viewer (block 530) and repeatthe steps described above to generate another recommendation score forthe additional candidate content. Once the recommendation processor hasfinished generating recommendation scores (block 528:N), therecommendation processor may generate a list of recommended content(block 532) and sort the list based on the respective recommendationscores obtained (block 534), e.g., in descending order of recommendationscore. The list of recommended content may include respective entriesthat each identify the content (e.g., the title of the content), therecommendation score for the content, and other types of informationassociated with the content. The list of recommended content may then beprovided to a device for presentation to the user (block 536), e.g., atelevision, computer monitor, display screen, audio speakers, or othertype of information output device. The user may then browse the list ofrecommended content items and select one of the recommended contentitems to view.

The steps described above may be respectively performed by variousdevices of a content delivery network. Such devices may include networklevel components or client devices. Network level components may includecomponents of a content delivery network, e.g., a server. Client devicesmay include gateway devices (e.g., STBs, DVRs), personal computingdevices (PCs), laptop computing devices, handheld mobile computingdevices, and display device (e.g., televisions, monitors, displayscreens). FIGS. 6A-B illustrate example implementations of aspects ofthe present disclosure.

In FIG. 6A, one example of an implementation of a system that providescontent recommendations is shown. In this example implementation, acontent delivery network 600 may include multiple network levelcomponents in signal communication with each other. One of the networklevel components may be a consumption history recording device 602 ahaving a data store 604 that stores multiple consumption histories 606for respective individuals. Another one of the network components may bea content recommendation device 602 b having a recommendation processor608 that obtains the recommendation scores and the list of recommendedcontent as well as similarity processor 610 that determines thesimilarity score for content pairings. The recommendation processor 608may select the content to recommend to an individual and retrieveinformation regarding previously consumed content from the consumptionhistory 606 for the individual. The similarity processor 610 may alsoretrieve information regarding previously consumed content from theconsumption histories 606 in order to determine the similarity score.Although the recommendation processor 608 and the similarity processor610 are shown as residing at the same content recommendation device 602b in FIG. 6A, it will be appreciated that the recommendation processorand the similarity processor may reside at different network levelcomponents in other example implementations. In addition therecommendation processor 608 and the similarity processor 610 may, insome examples, be implemented as a single processor that obtains therecommendations scores, the list of recommended content, and thesimilarity scores.

In FIG. 6B, another example of an implementation of a system thatprovides content recommendations is shown. In this other exampleimplementation, a content delivery network 650 includes a recommendationdevice 652 that includes each of the data store 654 storing theconsumption histories 656, the recommendation processor 658, and thesimilarity processor 660. It will be appreciated that the contentdelivery network 650 may include multiple recommendation devices such asrecommendation device 652 that each serve a respective service area,e.g., a respective geographic region. Accordingly a content deliverynetwork may therefore include multiple recommendation processors andmultiple similarity processors respectively distributed among multiplerecommendation devices. Additional and alternative implementations ofthe content delivery network that provides improved contentrecommendations will thus be appreciated with the benefit of thisdisclosure.

Referring now to FIG. 7, an example workflow between components of anexample of an implementation of a system 700 that provides video contentrecommendations is shown. The system 700 shown by way of example in FIG.7, may include a client device 702 in signal communication with acontent delivery network 704 via a network 707 such as those describedabove. The client device 702 may be similar to or the same as the clientdevice 112 a described above with reference to FIG. 1. The client device702 may also be in signal communication with an output device 705 suchas a video output device, audio output device, or audiovisual outputdevice. As also described above, the client device 702 may submit arecommendation request 706 to the content delivery network 704. Arecommendation device 708 of the content delivery network 704 mayreceive the recommendation request 706 and perform a contentrecommendation procedure such as the one described above. Therecommendation device 708 may be similar to or the same as therecommendation devices 602 b or 652 respectively described above withreference to FIGS. 6A-B.

Upon completion of the content recommendation procedure, therecommendation device 708 may prepare a recommendation response 710 andtransmit the recommendation response back to the client device 702. Asnoted above the recommendation device 708 or the client device 702 maybe configured to sort the list of recommended content based on therecommendation scores obtained. Accordingly in some exampleimplementations, the recommendation response 710 may include the list ofrecommended content sorted by the recommendation device 708. In otherexample implementations, the recommendation response 710 may onlyinclude the recommendation scores obtained at the recommendation device708, and the client device 702 may sort the list of recommended contentbased on the recommendations scores received. Additional and alternativeimplementations may also be employed, which will be appreciated with thebenefit of this disclosure. The client device 702 may then provide thelist of recommended content 712 to the output device 705 forpresentation to the individual. The individual may then browse the listof recommended content and select recommended content to consume.

Referring now to FIG. 8, a flowchart of an example method 800 forproviding content recommendations is shown. A recommendation system maybe configured, and the recommendation system may initiate arecommendation process (block 802). As described above, configuring therecommendation system may include compiling consumption histories ofcontent consumed by one or more individuals. Having initiated therecommendation process, the recommendation system may retrieve theconsumption history for an individual (block 804), and select candidatecontent to recommend to the individual (block 806). The recommendationsystem may then select an item of previously consumed content from theconsumption history for the individual (block 808) and determine a timedifference between the consumption time of the previously viewed contentand a reference time associated with the candidate content (block 810).The recommendation system may then obtain a contribution factor for thepreviously viewed content based on that time difference (block 812). Ifmore previously consumed content remains in the consumption history toevaluate (block 814:Y), the recommendations system may select the nextitem of previously consumed content and repeat these steps to obtain anadditional contribution factor for that additional item of previouslyconsumed content. Once the desired previously consumed content itemshave been evaluated (block 814:N), the recommendation system may obtaina recommendation score for the candidate content based, at least inpart, on the set of contribution factors obtained for the previouslyconsumed content items (block 816).

As explained in detail above, the recommendation system may employ thecontribution factors to improve the recommendations made byrecommendation techniques that utilize content similarity. It isemphasized, however, that the recommendation system may employ thecontribution factors to improve the recommendations made by otherrecommendation techniques that utilize other content characteristics. Asnoted above, another recommendation technique may make content menurecommendations based on content genre (e.g., drama, comedy, horror,etc.) In this approach, the recommendation system may obtain arecommendation score for each content menu (e.g., a drama menu, a comedymenu, a horror menu, etc.). The content menu recommendation scores maybe based on the previously consumed content within a particular genre(e.g., previously consumed drama content, previously consumed comedycontent, previously consumed horror content, etc.). Using the techniquesabove, contribution factors for the previously consumed content may beobtained based on the viewing periodicities observed with respect tocontent genre, e.g., viewing habits that indicate consumption of contentof the same genre x hours apart. Accordingly content consumed somemultiple of x hours previous will contribute relatively more to therecommendation score for the content menu of that genre via thecontribute factor obtained for that previously consumed content. In thisexample, the content menus may be presented to an individual ranked bytheir respective content menu recommendation scores. A recommendationsystem may also employ a hybrid recommendation technique in which both acontent menu recommendation score and individual candidate contentrecommendation scores are obtained. In this example, the recommendationsystem may obtain recommendation scores using the techniques describedabove and rank the content menus as well as the candidate content itemswithin those menus (e.g., using content similarity). Additional andalternative examples will be appreciated with the benefit of thisdisclosure.

Systems and methods for providing content recommendations have beendescribed above. As described above, content recommendations may begenerated based on the proximity of a time difference to a multiple of apredetermined time period (e.g., 24 hours) where that time difference isbased on a consumption time of consumed content and a reference timeassociated with unconsumed content available to be recommended. It willbe apparent to those skilled in the art that various modifications andvariations can be made without departing from the scope or spirit. Otherembodiments will be apparent to those skilled in the art fromconsideration of the specification and practice disclosed herein. Forexample, instead of applying a weight to a similarity score, thesimilarity score for content pairings may be adjusted higher or lowerbased on the proximity of the time difference to a multiple of thepredetermined time period in order to obtain an adjusted similarityscore. The similarity score, for example, may be adjusted to berelatively higher when the time difference is relatively closer to amultiple of the predetermined time period and adjusted to be relativelylower when the time difference is relatively further from a multiple ofthe predetermined time period. Content recommendations may then begenerated based, at least in part, on the adjusted similarity score. Itis intended that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims.

Aspects of the disclosure have been described in terms of illustrativeembodiments thereof. While illustrative systems and methods as describedherein embodying various aspects of the present disclosure are shown, itwill be understood by those skilled in the art, that the disclosure isnot limited to these embodiments. Modifications may be made by thoseskilled in the art, particularly in light of the foregoing teachings.

For example, the steps illustrated in the illustrative figures may beperformed in other than the recited order, and that one or more stepsillustrated may be optional in accordance with aspects of thedisclosure. It will also be appreciated and understood thatmodifications may be made without departing from the true spirit andscope of the present disclosure. The description is thus to be regardedas illustrative instead of restrictive on the present disclosure.

1. A method comprising: determining, by a computing device and based onan output history, a first plurality of previously outputted contentitems that were output a first time period multiple before a viewingoccasion; determining, by the computing device and based on the outputhistory, a second plurality of previously outputted content items thatwere output a second time period multiple before the viewing occasion;causing, for the viewing occasion, output of a content recommendationcomprising at least one recommended content item, wherein the contentrecommendation is based on a similarity between: the at least onerecommended content item; and one or more previously outputted contentitems of at least one of the first plurality of previously outputtedcontent items or the second plurality of previously outputted contentitems.
 2. The method of claim 1, wherein the content recommendationcomprises an indication of a ranking of a first recommended content itemof the at least one recommended content item and a second recommendedcontent item of the at least one recommended content item.
 3. The methodof claim 1, further comprising determining the similarity based on firstmetadata associated with the at least one recommended content item andsecond metadata associated with the one or more previously outputtedcontent items.
 4. The method of claim 1, wherein the contentrecommendation is further based on a number of times the at least onerecommended content item was previously output.
 5. The method of claim1, wherein the content recommendation is further based on a temporalproximity of an output of a previously outputted content item of the oneor more previously outputted content items to the viewing occasion. 6.The method of claim 1, wherein: the determining the first plurality ofpreviously outputted content items is further based on a temporalproximity of a first output time of a first previously outputted contentitem of the first plurality of previously outputted content items to atime associated with the viewing occasion; and the determining thesecond plurality of previously outputted content items is further basedon a temporal proximity of a second output time of a second previouslyoutputted content item of the second plurality of previously outputtedcontent items to the time associated with the viewing occasion.
 7. Themethod of claim 1, wherein: the determining the first plurality ofpreviously outputted content items comprises determining one or morefirst previously outputted content items that were output at firstoutput time within a threshold amount of time of a time associated withthe viewing occasion; and the determining the second plurality ofpreviously outputted content items comprises determining one or moresecond previously outputted content items that were output at a secondoutput time within a threshold amount of time of a time associated withthe viewing occasion.
 8. The method of claim 1, wherein one or more ofthe determining the first plurality of previously outputted contentitems or the second plurality of previously outputted content items isfurther based on an indication of a search for content.
 9. The method ofclaim 1, wherein the second time period multiple is a time periodmultiple of the first time period multiple.
 10. The method of claim 1,wherein: the first time period multiple is one or more days; the secondtime period multiple is one or more weeks; or the first time periodmultiple is one or more days and the second time period multiple is oneor more weeks.
 11. The method of claim 1, wherein the causing output ofthe content recommendation is based on an indication of the viewingoccasion, and wherein the indication of the viewing occasion comprisesone or more of: an indication of a request to display one or morebroadcast content items; an indication of a request to display one ormore recorded content items; or an indication of a request to displayone or more on-demand content items.
 12. The method of claim 1, whereinthe content recommendation comprises one or more of: an indication ofone or more recommended prescheduled content items; or an indication ofone or more recommended on-demand content items.
 13. The method of claim1, wherein the content recommendation comprises an indication of one ormore recommended recorded content items.
 14. The method of claim 1,wherein the content recommendation comprises an indication of one ormore recommended transmitted content items.
 15. The method of claim 1,wherein the at least one recommended content item comprises one or moreof: one or more recommended video content items; one or more recommendedaudio content items; one or more image content items; one or more textcontent items; or one or more interactive content items.
 16. The methodof claim 1, wherein the at least one recommended content item comprisesone or more of: one or more recommended movies; one or more recommendedtelevision programs that are recommended for output; one or morerecommended sports programs that are recommended for output; one or morerecommended radio programs that are recommended for output; one or morerecommended songs that are recommended for output; or one or morerecommended video games that are recommended for output.
 17. The methodof claim 1, wherein the computing device comprises: a client device; agateway device; a headend device; or a content delivery network device.18. The method of claim 1, wherein the causing output of the contentrecommendation comprises sending, to a second computing device via anetwork, the content recommendation.
 19. The method of claim 1, whereinthe causing output of the content recommendation is contemporaneous withthe viewing occasion.
 20. The method of claim 1, wherein the causingoutput of the content recommendation is before the viewing occasion. 21.The method of claim 1, wherein the content recommendation is furtherbased on a number of users that previously output both a recommendedcontent item of the at least one recommended content item and apreviously outputted content item of the one or more previouslyoutputted content items.